Since its inception, biometric sensing technology, such as fingerprint sensing, has revolutionized identification and authentication processes. The ability to capture and store biometric data in a digital file of minimal size has yielded immense benefits in fields such as law enforcement, forensics, and information security.
Utilizing fingerprints in a biometric authentication process typically includes storing one or more fingerprint images captured by a fingerprint sensor as a fingerprint template for later authentication. During the authentication process, a newly acquired fingerprint image is received and compared to the fingerprint template to determine whether there is a match. Before the newly acquired fingerprint image can be compared to the fingerprint template, the newly acquired fingerprint image is aligned by performing a transformation to the newly acquired fingerprint image. The transformation may include one or more of rotation, translation (in two dimensions), and scaling of the newly acquired fingerprint image. This process is known as image alignment.
However, image alignment is a challenging problem when the newly acquired fingerprint image and the template image are low quality or if only a small part of one image overlaps with a sub-part of the other image. With increased use of smaller image sensors, the amount of overlap among the images is decreasing, which further decreases the effectiveness of conventional image alignment techniques. In addition, if a purely minutiae-based technique is used for image alignment or image matching, the use of smaller sensors decreases the number of minutiae points in the images, which decreases even further the effectiveness of conventional image alignment and image matching techniques.
Accordingly, there remains a need in the art for systems and methods for image alignment that address the deficiencies of conventional approaches.